High‐Performance Suspended Particle Devices Based on Copper‐Reduced Graphene Oxide Core–Shell Nanowire Electrodes
Bibliographic record
Abstract
Abstract Smart windows are one of the key components of so‐called “green” buildings. These windows are based on an actively switchable electro‐optic material that is sandwiched between two transparent electrodes. Although great progress has been made in identifying the optimal materials for such active windows, there is still a great need to improve their key elements, especially the performance of the transparent electrodes. Here, a new suspended particle device (SPD), holding a great potential for smart window applications, which is built upon copper‐reduced graphene oxide (Cu‐rGO) core–shell nanowire (NW) films as a transparent conductive electrode is reported. With the wrapping of rGO, the Cu NW electrodes demonstrate both high optical transparency and electrical conductivity, as well as significantly improved stability under various testing conditions. The novel sandwich‐structured SPDs, based on these electrodes, show a large change in their optical transmittance (42%) between “on” and “off” states, impressively fast switching time and superior stability. These high performances are comparable to those of the SPDs based on indium tin oxide electrodes. These promising results pave the way for the electrodes to be an integral part of a variety of optoelectronic devices, including energy‐friendly and flexible electronics.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".